Commercetools Python API Docs | dltHub

Commercetools is a composable commerce platform providing HTTP REST and GraphQL APIs to manage e-commerce resources (products, orders, customers, carts, etc.). The REST API base URL is https://api.{region}.commercetools.com/{projectKey} and all requests require an OAuth2 Bearer token (client credentials for server-to-server).

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Commercetools data in under 10 minutes.

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What data can I load from Commercetools?

Here are some of the endpoints you can load from Commercetools:

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How do I authenticate with the Commercetools API?

The HTTP API uses OAuth 2.0. For machine-to-machine access, obtain a bearer access token via the client credentials flow by POSTing to the Commercetools authorization host and include Authorization: Bearer in requests. Use Content-Type: application/json for JSON bodies where applicable.

1. Get your credentials

1. Open Merchant Center -> Settings -> API Clients (or use the API Clients API). 2) Create an API Client and grant required scopes (e.g. view_orders:{projectKey} or manage_project:{projectKey}). 3) Note the client_id and client_secret. 4) Request an access token with client credentials (HTTP Basic auth using client_id:client_secret) to the auth host /oauth/token with grant_type=client_credentials and scope parameter.

2. Add them to .dlt/secrets.toml

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[sources.commercetools_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
project_key = "your_project_key"
region = "your_region"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Commercetools API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python commercetools_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline commercetools_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset commercetools_data
The duckdb destination used duckdb:/commercetools.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline commercetools_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads orders and order from the Commercetools API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def commercetools_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.{region}.commercetools.com/{projectKey}",
            "auth": {
                "type": "bearer",
                "client_secret": client_secret,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "orders", "data_selector": "results"}},
            {"name": "order", "endpoint": {"path": "orders/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="commercetools_pipeline",
        destination="duckdb",
        dataset_name="commercetools_data",
    )
    load_info = pipeline.run(commercetools_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("commercetools_pipeline").dataset()
sessions_df = data.orders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM commercetools_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("commercetools_pipeline").dataset()
data.orders.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Commercetools data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 Unauthorized: verify that you requested an access token with the correct client_id/client_secret and scopes, and include Authorization: Bearer header. Obtain tokens from the Commercetools auth host via the client credentials flow.

Rate limits and throttling

Commercetools enforces quotas per project/region. If you receive 429 Too Many Requests, implement exponential backoff and retry after the Retry-After header if provided.

Pagination and large queries

Many GET list endpoints return a paged response (e.g. OrderPagedQueryResponse) containing a results array plus limit, offset, and total fields. Use limit/offset query parameters (max limit 500) or predicates to page through results. Set withTotal=false to skip total count for better performance.

Common error responses

• 400 Bad Request: malformed request or invalid predicate
• 401 Unauthorized: invalid or missing bearer token
• 403 Forbidden: insufficient scopes for requested operation
• 404 Not Found: resource not found
• 409 Conflict: version mismatch on update/delete
• 429 Too Many Requests: rate limit exceeded
• 500 Internal Server Error: server-side problem; verify resource state after retries

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack